install.packages("DT")
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ tibble  3.0.4     ✓ dplyr   1.0.2
✓ tidyr   1.1.2     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.0
✓ purrr   0.3.4     
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library(forecast)
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
This is forecast 8.13 
  Need help getting started? Try the online textbook FPP:
  http://otexts.com/fpp2/
library(caTools)
library(earth)
Loading required package: Formula
Loading required package: plotmo
Loading required package: plotrix
Loading required package: TeachingDemos

Attaching package: ‘TeachingDemos’

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library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.

Attaching package: ‘randomForest’

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library(h2o)

----------------------------------------------------------------------

Your next step is to start H2O:
    > h2o.init()

For H2O package documentation, ask for help:
    > ??h2o

After starting H2O, you can use the Web UI at http://localhost:54321
For more information visit https://docs.h2o.ai

----------------------------------------------------------------------


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    is.numeric, log, log10, log1p, log2, round, signif, trunc
library(neuralnet)

Attaching package: ‘neuralnet’

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library(Metrics)

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library(caret)
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library(hms)
library(lubridate)

Attaching package: ‘lubridate’

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library(ggplot2)
library(gganimate)
library(gapminder)
library(gifski)
library(png)
library(shiny)
library(plotly)
library(shinydashboard)
library(DT)

DATA

Mall_customers <- read.csv("Mall_Customers.csv", stringsAsFactors = T)

Mall_customers <- Mall_customers %>% rename(
  Annual_Income = Annual.Income..k..,
  Spending_Score = Spending.Score..1.100.)

Mall_customers <- Mall_customers %>%
  select(Genre, Age, Annual_Income, Spending_Score)

Mall_customers <- Mall_customers %>% mutate(
  Gender = if_else(Genre == "Male", true = 1, false = 0)
) %>% 
  select(-Genre)
Mall_customers
summary(Mall_customers)
      Age        Annual_Income    Spending_Score      Gender    
 Min.   :18.00   Min.   : 15.00   Min.   : 1.00   Min.   :0.00  
 1st Qu.:28.75   1st Qu.: 41.50   1st Qu.:34.75   1st Qu.:0.00  
 Median :36.00   Median : 61.50   Median :50.00   Median :0.00  
 Mean   :38.85   Mean   : 60.56   Mean   :50.20   Mean   :0.44  
 3rd Qu.:49.00   3rd Qu.: 78.00   3rd Qu.:73.00   3rd Qu.:1.00  
 Max.   :70.00   Max.   :137.00   Max.   :99.00   Max.   :1.00  

DATA SPLIT

set.seed(123)

sample = sample.split(Mall_customers$Gender, SplitRatio = .75)
train = subset(Mall_customers, sample == TRUE)
test = subset(Mall_customers, sample == FALSE)

RAMDOM FOREST

# Random Forest Model
RFModel <- randomForest(Gender ~ ., data = train, mtry = 3, ntree = 64)
The response has five or fewer unique values.  Are you sure you want to do regression?
RFModel

Call:
 randomForest(formula = Gender ~ ., data = train, mtry = 3, ntree = 64) 
               Type of random forest: regression
                     Number of trees: 64
No. of variables tried at each split: 3

          Mean of squared residuals: 0.2689081
                    % Var explained: -9.13
plot(RFModel)


# Prediction
RFPrediction <- predict(RFModel, test, type = "class")
RFPrediction
         2          3         11         13         15         19         24         29         32         34         35 
0.29843750 0.59791667 0.51250000 0.48281250 0.26458333 0.15338542 0.48802083 0.14583333 0.29895833 0.57890625 0.33984375 
        39         44         45         48         54         56         61         63         73         76         77 
0.14973958 0.29973958 0.17369792 0.09427083 0.46458333 0.12161458 0.50494792 0.70416667 0.63984375 0.39713542 0.21588542 
        78         82         84         85         87         95        106        107        121        128        137 
0.21354167 0.22213542 0.18385417 0.43671875 0.33723958 0.10729167 0.37187500 0.93046875 0.28020833 0.79479167 0.82239583 
       138        149        150        152        153        157        159        160        162        166        182 
0.12473958 0.35130208 0.36067708 0.41822917 0.71067708 0.83203125 0.84140625 0.08463542 0.05312500 0.49843750 0.32656250 
       184        188        189        191        199        200 
0.42864583 0.57578125 0.27708333 0.26223958 0.51041667 0.53385417 
# Generate Confusion Matrix
RFconfusionMatrix <- table(RFPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
RFaccuracy <- round(sum(diag(RFconfusionMatrix))/ sum(RFconfusionMatrix), digits = 4)
cat("Random Forest Accuracy:", RFaccuracy)
Random Forest Accuracy: 0.02
# MSE
#Metrics::mse(test, RFPrediction)

#SVM

# Support Vector Machine Model
SVMmodel <- ksvm(Gender ~ .,
                 data = train,
                 kernel = "vanilladot")
 Setting default kernel parameters  
SVMmodel
Support Vector Machine object of class "ksvm" 

SV type: eps-svr  (regression) 
 parameter : epsilon = 0.1  cost C = 1 

Linear (vanilla) kernel function. 

Number of Support Vectors : 135 

Objective Function Value : -119.3168 
Training error : 1.606914 
# Prediction
SVMpred <- predict(SVMmodel, test)

# Generate Confusion Matrix
SVMconfusionMatrix <- table(SVMpred,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
SVMaccuracy <- round(sum(diag(SVMconfusionMatrix))/ sum(SVMconfusionMatrix), digits = 4)
cat("Support Vector Machine Accuracy:", SVMaccuracy)
Support Vector Machine Accuracy: 0
# MSE
#Metrics::mse(test, SVMpred)

NEURAL NETWORKS

# Neural Networks Model
NNModel <- neuralnet(Gender ~ .,
                     data = train)
NNModel
$call
neuralnet(formula = Gender ~ ., data = train)

$response
    Gender
1        1
4        0
5        0
6        0
7        0
8        0
9        1
10       0
12       0
14       0
16       1
17       0
18       1
20       0
21       1
22       1
23       0
25       0
26       1
27       0
28       1
30       0
31       1
33       1
36       0
37       0
38       0
40       0
41       0
42       1
43       1
46       0
47       0
49       0
50       0
51       0
52       1
53       0
55       0
57       0
58       1
59       0
60       1
62       1
64       0
65       1
66       1
67       0
68       0
69       1
70       0
71       1
72       0
74       0
75       1
79       0
80       0
81       1
83       1
86       1
88       0
89       0
90       0
91       0
92       1
93       1
94       0
96       1
97       0
98       0
99       1
100      1
101      0
102      0
103      1
104      1
105      1
108      1
109      1
110      1
111      1
112      0
113      0
114      1
115      0
116      0
117      0
118      0
119      0
120      0
122      0
123      0
124      1
125      0
126      0
127      1
129      1
130      1
131      1
132      1
133      0
134      0
135      1
136      0
139      1
140      0
141      0
142      1
143      0
144      0
145      1
146      1
147      1
148      0
151      1
154      0
155      0
156      0
158      0
161      0
163      1
164      0
165      1
167      1
168      0
169      0
170      1
171      1
172      1
173      1
174      1
175      0
176      0
177      1
178      1
179      1
180      1
181      0
183      1
185      0
186      1
187      0
190      0
192      0
193      1
194      0
195      0
196      0
197      0
198      1

$covariate
    Age Annual_Income Spending_Score
1    19            15             39
4    23            16             77
5    31            17             40
6    22            17             76
7    35            18              6
8    23            18             94
9    64            19              3
10   30            19             72
12   35            19             99
14   24            20             77
16   22            20             79
17   35            21             35
18   20            21             66
20   35            23             98
21   35            24             35
22   25            24             73
23   46            25              5
25   54            28             14
26   29            28             82
27   45            28             32
28   35            28             61
30   23            29             87
31   60            30              4
33   53            33              4
36   21            33             81
37   42            34             17
38   30            34             73
40   20            37             75
41   65            38             35
42   24            38             92
43   48            39             36
46   24            39             65
47   50            40             55
49   29            40             42
50   31            40             42
51   49            42             52
52   33            42             60
53   31            43             54
55   50            43             45
57   51            44             50
58   69            44             46
59   27            46             51
60   53            46             46
62   19            46             55
64   54            47             59
65   63            48             51
66   18            48             59
67   43            48             50
68   68            48             48
69   19            48             59
70   32            48             47
71   70            49             55
72   47            49             42
74   60            50             56
75   59            54             47
79   23            54             52
80   49            54             42
81   57            54             51
83   67            54             41
86   48            54             46
88   22            57             55
89   34            58             60
90   50            58             46
91   68            59             55
92   18            59             41
93   48            60             49
94   40            60             40
96   24            60             52
97   47            60             47
98   27            60             50
99   48            61             42
100  20            61             49
101  23            62             41
102  49            62             48
103  67            62             59
104  26            62             55
105  49            62             56
108  54            63             46
109  68            63             43
110  66            63             48
111  65            63             52
112  19            63             54
113  38            64             42
114  19            64             46
115  18            65             48
116  19            65             50
117  63            65             43
118  49            65             59
119  51            67             43
120  50            67             57
122  38            67             40
123  40            69             58
124  39            69             91
125  23            70             29
126  31            70             77
127  43            71             35
129  59            71             11
130  38            71             75
131  47            71              9
132  39            71             75
133  25            72             34
134  31            72             71
135  20            73              5
136  29            73             88
139  19            74             10
140  35            74             72
141  57            75              5
142  32            75             93
143  28            76             40
144  32            76             87
145  25            77             12
146  28            77             97
147  48            77             36
148  32            77             74
151  43            78             17
154  38            78             76
155  47            78             16
156  27            78             89
158  30            78             78
161  56            79             35
163  19            81              5
164  31            81             93
165  50            85             26
167  42            86             20
168  33            86             95
169  36            87             27
170  32            87             63
171  40            87             13
172  28            87             75
173  36            87             10
174  36            87             92
175  52            88             13
176  30            88             86
177  58            88             15
178  27            88             69
179  59            93             14
180  35            93             90
181  37            97             32
183  46            98             15
185  41            99             39
186  30            99             97
187  54           101             24
190  36           103             85
192  32           103             69
193  33           113              8
194  38           113             91
195  47           120             16
196  35           120             79
197  45           126             28
198  32           126             74

$model.list
$model.list$response
[1] "Gender"

$model.list$variables
[1] "Age"            "Annual_Income"  "Spending_Score"


$err.fct
function (x, y) 
{
    1/2 * (y - x)^2
}
<bytecode: 0x7f8e78d00c38>
<environment: 0x7f8e78d034c0>
attr(,"type")
[1] "sse"

$act.fct
function (x) 
{
    1/(1 + exp(-x))
}
<bytecode: 0x7f8e78cf6710>
<environment: 0x7f8e78cfbd28>
attr(,"type")
[1] "logistic"

$linear.output
[1] TRUE

$data
    Age Annual_Income Spending_Score Gender
1    19            15             39      1
4    23            16             77      0
5    31            17             40      0
6    22            17             76      0
7    35            18              6      0
8    23            18             94      0
9    64            19              3      1
10   30            19             72      0
12   35            19             99      0
14   24            20             77      0
16   22            20             79      1
17   35            21             35      0
18   20            21             66      1
20   35            23             98      0
21   35            24             35      1
22   25            24             73      1
23   46            25              5      0
25   54            28             14      0
26   29            28             82      1
27   45            28             32      0
28   35            28             61      1
30   23            29             87      0
31   60            30              4      1
33   53            33              4      1
36   21            33             81      0
37   42            34             17      0
38   30            34             73      0
40   20            37             75      0
41   65            38             35      0
42   24            38             92      1
43   48            39             36      1
46   24            39             65      0
47   50            40             55      0
49   29            40             42      0
50   31            40             42      0
51   49            42             52      0
52   33            42             60      1
53   31            43             54      0
55   50            43             45      0
57   51            44             50      0
58   69            44             46      1
59   27            46             51      0
60   53            46             46      1
62   19            46             55      1
64   54            47             59      0
65   63            48             51      1
66   18            48             59      1
67   43            48             50      0
68   68            48             48      0
69   19            48             59      1
70   32            48             47      0
71   70            49             55      1
72   47            49             42      0
74   60            50             56      0
75   59            54             47      1
79   23            54             52      0
80   49            54             42      0
81   57            54             51      1
83   67            54             41      1
86   48            54             46      1
88   22            57             55      0
89   34            58             60      0
90   50            58             46      0
91   68            59             55      0
92   18            59             41      1
93   48            60             49      1
94   40            60             40      0
96   24            60             52      1
97   47            60             47      0
98   27            60             50      0
99   48            61             42      1
100  20            61             49      1
101  23            62             41      0
102  49            62             48      0
103  67            62             59      1
104  26            62             55      1
105  49            62             56      1
108  54            63             46      1
109  68            63             43      1
110  66            63             48      1
111  65            63             52      1
112  19            63             54      0
113  38            64             42      0
114  19            64             46      1
115  18            65             48      0
116  19            65             50      0
117  63            65             43      0
118  49            65             59      0
119  51            67             43      0
120  50            67             57      0
122  38            67             40      0
123  40            69             58      0
124  39            69             91      1
125  23            70             29      0
126  31            70             77      0
127  43            71             35      1
129  59            71             11      1
130  38            71             75      1
131  47            71              9      1
132  39            71             75      1
133  25            72             34      0
134  31            72             71      0
135  20            73              5      1
136  29            73             88      0
139  19            74             10      1
140  35            74             72      0
141  57            75              5      0
142  32            75             93      1
143  28            76             40      0
144  32            76             87      0
145  25            77             12      1
146  28            77             97      1
147  48            77             36      1
148  32            77             74      0
151  43            78             17      1
154  38            78             76      0
155  47            78             16      0
156  27            78             89      0
158  30            78             78      0
161  56            79             35      0
163  19            81              5      1
164  31            81             93      0
165  50            85             26      1
167  42            86             20      1
168  33            86             95      0
169  36            87             27      0
170  32            87             63      1
171  40            87             13      1
172  28            87             75      1
173  36            87             10      1
174  36            87             92      1
175  52            88             13      0
176  30            88             86      0
177  58            88             15      1
178  27            88             69      1
179  59            93             14      1
180  35            93             90      1
181  37            97             32      0
183  46            98             15      1
185  41            99             39      0
186  30            99             97      1
187  54           101             24      0
190  36           103             85      0
192  32           103             69      0
193  33           113              8      1
194  38           113             91      0
195  47           120             16      0
196  35           120             79      0
197  45           126             28      0
198  32           126             74      1

$exclude
NULL

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$weights
$weights[[1]]
$weights[[1]][[1]]
           [,1]
[1,]  1.3857495
[2,] -0.1667256
[3,] -0.1208851
[4,] -0.3709971

$weights[[1]][[2]]
           [,1]
[1,]  0.4400289
[2,] -2.0001810



$generalized.weights
$generalized.weights[[1]]
            [,1]         [,2]         [,3]
1   1.933043e-08 1.401561e-08 4.301400e-08
4   6.629387e-15 4.806665e-15 1.475169e-14
5   1.416524e-09 1.027057e-09 3.152045e-09
6   1.005781e-14 7.292461e-15 2.238061e-14
7   1.937212e-04 1.404584e-04 4.310676e-04
8   9.492917e-18 6.882880e-18 2.112360e-17
9   4.152155e-06 3.010538e-06 9.239360e-06
10  9.177581e-15 6.654244e-15 2.042192e-14
12  1.779893e-19 1.290519e-19 3.960611e-19
14  3.459923e-15 2.508632e-15 7.699007e-15
16  2.299526e-15 1.667281e-15 5.116898e-15
17  2.865518e-09 2.077656e-09 6.376340e-09
18  3.536233e-13 2.563961e-13 7.868812e-13
20  1.590439e-19 1.153154e-19 3.539038e-19
21  1.993903e-09 1.445688e-09 4.436825e-09
22  7.964299e-15 5.774549e-15 1.772213e-14
23  1.924732e-05 1.395535e-05 4.282906e-05
25  1.251743e-07 9.075817e-08 2.785374e-07
26  8.941667e-17 6.483194e-17 1.989696e-16
27  7.063031e-10 5.121080e-10 1.571663e-09
28  7.953032e-14 5.766380e-14 1.769706e-13
30  3.370984e-17 2.444147e-17 7.501102e-17
31  1.476684e-06 1.070676e-06 3.285911e-06
33  3.300986e-06 2.393394e-06 7.345341e-06
36  2.687237e-16 1.948392e-16 5.979628e-16
37  1.472471e-07 1.067622e-07 3.276538e-07
38  1.033024e-15 7.489981e-16 2.298680e-15
40  1.813206e-15 1.314672e-15 4.034738e-15
41  2.468696e-12 1.789939e-12 5.493334e-12
42  1.503977e-18 1.090465e-18 3.346643e-18
43  2.569106e-11 1.862741e-11 5.716764e-11
46  2.985628e-14 2.164742e-14 6.643608e-14
47  1.416276e-14 1.026877e-14 3.151493e-14
49  5.838513e-11 4.233239e-11 1.299184e-10
50  4.182984e-11 3.032891e-11 9.307961e-11
51  3.998735e-14 2.899300e-14 8.897969e-14
52  2.961246e-14 2.147064e-14 6.589354e-14
53  3.392524e-13 2.459764e-13 7.549031e-13
55  4.025955e-13 2.919036e-13 8.958540e-13
57  4.724449e-14 3.425482e-14 1.051283e-13
58  1.036323e-14 7.513904e-15 2.306022e-14
59  1.399657e-12 1.014828e-12 3.114512e-12
60  1.172259e-13 8.499517e-14 2.608507e-13
62  1.204475e-12 8.733096e-13 2.680193e-12
64  7.071798e-16 5.127437e-16 1.573614e-15
65  2.718530e-15 1.971082e-15 6.049263e-15
66  2.533484e-13 1.836914e-13 5.637500e-13
67  1.105646e-13 8.016536e-14 2.460280e-13
68  3.594680e-15 2.606338e-15 7.998868e-15
69  2.144422e-13 1.554822e-13 4.771759e-13
70  2.106058e-12 1.527006e-12 4.686392e-12
71  1.700162e-16 1.232710e-16 3.783195e-16
72  9.782742e-13 7.093019e-13 2.176852e-12
74  5.507426e-16 3.993183e-16 1.225510e-15
75  1.130982e-14 8.200236e-15 2.516658e-14
79  7.153753e-13 5.186859e-13 1.591850e-12
80  3.829534e-13 2.776620e-13 8.521465e-13
81  3.579182e-15 2.595101e-15 7.964383e-15
83  2.760094e-14 2.001218e-14 6.141751e-14
86  1.025808e-13 7.437662e-14 2.282623e-13
88  1.932309e-13 1.401029e-13 4.299768e-13
89  3.623021e-15 2.626887e-15 8.061933e-15
90  4.531594e-14 3.285651e-14 1.008369e-13
91  7.084498e-17 5.136645e-17 1.576440e-16
92  5.326199e-11 3.861783e-11 1.185184e-10
93  1.631930e-14 1.183238e-14 3.631364e-14
94  1.746078e-12 1.266001e-12 3.885366e-12
96  2.931754e-13 2.125681e-13 6.523728e-13
97  4.049054e-14 2.935783e-14 9.009939e-14
98  3.733770e-13 2.707186e-13 8.308370e-13
99  1.941135e-13 1.407429e-13 4.319407e-13
100 1.540373e-12 1.116854e-12 3.427632e-12
101 1.610194e-11 1.167478e-11 3.582998e-11
102 1.571868e-14 1.139689e-14 3.497714e-14
103 1.320474e-17 9.574153e-18 2.938314e-17
104 5.419335e-14 3.929312e-14 1.205908e-13
105 8.080532e-16 5.858824e-16 1.798077e-15
108 1.270926e-14 9.214904e-15 2.828060e-14
109 3.747782e-15 2.717345e-15 8.339550e-15
110 8.184276e-16 5.934044e-16 1.821162e-15
111 2.192301e-16 1.589537e-16 4.878300e-16
112 2.235756e-13 1.621044e-13 4.974995e-13
113 7.155446e-13 5.188086e-13 1.592227e-12
114 3.853902e-12 2.794288e-12 8.575689e-12
115 1.921166e-12 1.392950e-12 4.274972e-12
116 7.743065e-13 5.614142e-13 1.722984e-12
117 6.773527e-15 4.911175e-15 1.507243e-14
118 1.847459e-16 1.339508e-16 4.110958e-16
119 3.932887e-14 2.851556e-14 8.751445e-14
120 2.578762e-16 1.869742e-16 5.738252e-16
122 1.045638e-12 7.581446e-13 2.326751e-12
123 7.402354e-16 5.367108e-16 1.647169e-15
124 4.214552e-21 3.055779e-21 9.378205e-21
125 5.252239e-10 3.808158e-10 1.168726e-09
126 2.554018e-18 1.851801e-18 5.683190e-18
127 1.790411e-12 1.298145e-12 3.984015e-12
129 9.148563e-10 6.633205e-10 2.035735e-09
130 1.479490e-18 1.072711e-18 3.292156e-18
131 1.420669e-08 1.030062e-08 3.161268e-08
132 1.252288e-18 9.079766e-19 2.786586e-18
133 4.622948e-11 3.351888e-11 1.028697e-10
134 1.857618e-17 1.346874e-17 4.133565e-17
135 4.436047e-06 3.216375e-06 9.871076e-06
136 4.189851e-20 3.037869e-20 9.323240e-20
139 7.266003e-07 5.268247e-07 1.616828e-06
140 5.166578e-18 3.746050e-18 1.149665e-17
141 7.292930e-09 5.287770e-09 1.622820e-08
142 3.121512e-21 2.263266e-21 6.945977e-21
143 1.866225e-12 1.353114e-12 4.152717e-12
144 2.562108e-20 1.857667e-20 5.701192e-20
145 8.853288e-08 6.419114e-08 1.970030e-07
146 1.082703e-21 7.850188e-22 2.409228e-21
147 2.598916e-13 1.884355e-13 5.783098e-13
148 2.822824e-18 2.046701e-18 6.281337e-18
151 6.104509e-10 4.426100e-10 1.358373e-09
154 4.380181e-19 3.175869e-19 9.746762e-19
155 4.540888e-10 3.292390e-10 1.010437e-09
156 2.204925e-20 1.598691e-20 4.906392e-20
158 7.916114e-19 5.739612e-19 1.761491e-18
161 7.792033e-14 5.649646e-14 1.733880e-13
163 1.992540e-06 1.444700e-06 4.433793e-06
164 1.785561e-21 1.294628e-21 3.973223e-21
165 2.892001e-12 2.096858e-12 6.435271e-12
167 9.009379e-11 6.532289e-11 2.004764e-10
168 3.328250e-22 2.413162e-22 7.406010e-22
169 1.617294e-11 1.172626e-11 3.598797e-11
170 4.989174e-17 3.617422e-17 1.110189e-16
171 1.495771e-09 1.084515e-09 3.328385e-09
172 1.132913e-18 8.214233e-19 2.520953e-18
173 8.868768e-09 6.430338e-09 1.973475e-08
174 5.443250e-22 3.946651e-22 1.211230e-21
175 1.792543e-10 1.299691e-10 3.988759e-10
176 1.214895e-20 8.808646e-21 2.703379e-20
177 3.138900e-11 2.275873e-11 6.984668e-11
178 1.098593e-17 7.965396e-18 2.444585e-17
179 2.103744e-11 1.525328e-11 4.681242e-11
180 6.539009e-22 4.741136e-22 1.455058e-21
181 6.394016e-13 4.636008e-13 1.422794e-12
183 6.929089e-11 5.023966e-11 1.541858e-10
185 1.919942e-14 1.392062e-14 4.272248e-14
186 5.428745e-23 3.936135e-23 1.208002e-22
187 4.506245e-13 3.267272e-13 1.002728e-12
190 1.056128e-21 7.657503e-22 2.350093e-21
192 7.785758e-19 5.645097e-19 1.732484e-18
193 1.325374e-09 9.609678e-10 2.949216e-09
194 2.438772e-23 1.768242e-23 5.426747e-23
195 2.832384e-12 2.053632e-12 6.302609e-12
196 1.480340e-21 1.073326e-21 3.294046e-21
197 2.231031e-14 1.617618e-14 4.964481e-14
198 7.554349e-21 5.477313e-21 1.680991e-20


$startweights
$startweights[[1]]
$startweights[[1]][[1]]
            [,1]
[1,]  0.89214945
[2,] -0.83512559
[3,]  0.53658644
[4,]  0.04892574

$startweights[[1]][[2]]
           [,1]
[1,] -0.4243741
[2,] -1.5065810



$result.matrix
                                   [,1]
error                      18.479874846
reached.threshold           0.004433007
steps                      35.000000000
Intercept.to.1layhid1       1.385749453
Age.to.1layhid1            -0.166725588
Annual_Income.to.1layhid1  -0.120885097
Spending_Score.to.1layhid1 -0.370997144
Intercept.to.Gender         0.440028940
1layhid1.to.Gender         -2.000180983

attr(,"class")
[1] "nn"
# Visualization
NNModelPlot <- plot(NNModel)

NNModelPlot
NULL
# Prediction
NNPrediction <- predict(NNModel, test)

# Generate Confusion Matrix
NNconfusionMatrix <- table(NNPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
NNaccuracy <- round(sum(diag(NNconfusionMatrix))/ sum(NNconfusionMatrix), digits = 4)
cat("Neural Network Model Accuracy:", NNaccuracy)
Neural Network Model Accuracy: 0.04
# MSE
#Metrics::mse(test, NNPrediction)

DEEP LEARNING NEURAL NETWORKS

# Neural Networks Model
DNNModel <- neuralnet(Gender ~ .,
                     data = train,
                     hidden = 4)
DNNModel
$call
neuralnet(formula = Gender ~ ., data = train, hidden = 4)

$response
    Gender
1        1
4        0
5        0
6        0
7        0
8        0
9        1
10       0
12       0
14       0
16       1
17       0
18       1
20       0
21       1
22       1
23       0
25       0
26       1
27       0
28       1
30       0
31       1
33       1
36       0
37       0
38       0
40       0
41       0
42       1
43       1
46       0
47       0
49       0
50       0
51       0
52       1
53       0
55       0
57       0
58       1
59       0
60       1
62       1
64       0
65       1
66       1
67       0
68       0
69       1
70       0
71       1
72       0
74       0
75       1
79       0
80       0
81       1
83       1
86       1
88       0
89       0
90       0
91       0
92       1
93       1
94       0
96       1
97       0
98       0
99       1
100      1
101      0
102      0
103      1
104      1
105      1
108      1
109      1
110      1
111      1
112      0
113      0
114      1
115      0
116      0
117      0
118      0
119      0
120      0
122      0
123      0
124      1
125      0
126      0
127      1
129      1
130      1
131      1
132      1
133      0
134      0
135      1
136      0
139      1
140      0
141      0
142      1
143      0
144      0
145      1
146      1
147      1
148      0
151      1
154      0
155      0
156      0
158      0
161      0
163      1
164      0
165      1
167      1
168      0
169      0
170      1
171      1
172      1
173      1
174      1
175      0
176      0
177      1
178      1
179      1
180      1
181      0
183      1
185      0
186      1
187      0
190      0
192      0
193      1
194      0
195      0
196      0
197      0
198      1

$covariate
    Age Annual_Income Spending_Score
1    19            15             39
4    23            16             77
5    31            17             40
6    22            17             76
7    35            18              6
8    23            18             94
9    64            19              3
10   30            19             72
12   35            19             99
14   24            20             77
16   22            20             79
17   35            21             35
18   20            21             66
20   35            23             98
21   35            24             35
22   25            24             73
23   46            25              5
25   54            28             14
26   29            28             82
27   45            28             32
28   35            28             61
30   23            29             87
31   60            30              4
33   53            33              4
36   21            33             81
37   42            34             17
38   30            34             73
40   20            37             75
41   65            38             35
42   24            38             92
43   48            39             36
46   24            39             65
47   50            40             55
49   29            40             42
50   31            40             42
51   49            42             52
52   33            42             60
53   31            43             54
55   50            43             45
57   51            44             50
58   69            44             46
59   27            46             51
60   53            46             46
62   19            46             55
64   54            47             59
65   63            48             51
66   18            48             59
67   43            48             50
68   68            48             48
69   19            48             59
70   32            48             47
71   70            49             55
72   47            49             42
74   60            50             56
75   59            54             47
79   23            54             52
80   49            54             42
81   57            54             51
83   67            54             41
86   48            54             46
88   22            57             55
89   34            58             60
90   50            58             46
91   68            59             55
92   18            59             41
93   48            60             49
94   40            60             40
96   24            60             52
97   47            60             47
98   27            60             50
99   48            61             42
100  20            61             49
101  23            62             41
102  49            62             48
103  67            62             59
104  26            62             55
105  49            62             56
108  54            63             46
109  68            63             43
110  66            63             48
111  65            63             52
112  19            63             54
113  38            64             42
114  19            64             46
115  18            65             48
116  19            65             50
117  63            65             43
118  49            65             59
119  51            67             43
120  50            67             57
122  38            67             40
123  40            69             58
124  39            69             91
125  23            70             29
126  31            70             77
127  43            71             35
129  59            71             11
130  38            71             75
131  47            71              9
132  39            71             75
133  25            72             34
134  31            72             71
135  20            73              5
136  29            73             88
139  19            74             10
140  35            74             72
141  57            75              5
142  32            75             93
143  28            76             40
144  32            76             87
145  25            77             12
146  28            77             97
147  48            77             36
148  32            77             74
151  43            78             17
154  38            78             76
155  47            78             16
156  27            78             89
158  30            78             78
161  56            79             35
163  19            81              5
164  31            81             93
165  50            85             26
167  42            86             20
168  33            86             95
169  36            87             27
170  32            87             63
171  40            87             13
172  28            87             75
173  36            87             10
174  36            87             92
175  52            88             13
176  30            88             86
177  58            88             15
178  27            88             69
179  59            93             14
180  35            93             90
181  37            97             32
183  46            98             15
185  41            99             39
186  30            99             97
187  54           101             24
190  36           103             85
192  32           103             69
193  33           113              8
194  38           113             91
195  47           120             16
196  35           120             79
197  45           126             28
198  32           126             74

$model.list
$model.list$response
[1] "Gender"

$model.list$variables
[1] "Age"            "Annual_Income"  "Spending_Score"


$err.fct
function (x, y) 
{
    1/2 * (y - x)^2
}
<bytecode: 0x7f8e78d00c38>
<environment: 0x7f8e89b4a058>
attr(,"type")
[1] "sse"

$act.fct
function (x) 
{
    1/(1 + exp(-x))
}
<bytecode: 0x7f8e78cf6710>
<environment: 0x7f8e89b49bc0>
attr(,"type")
[1] "logistic"

$linear.output
[1] TRUE

$data
    Age Annual_Income Spending_Score Gender
1    19            15             39      1
4    23            16             77      0
5    31            17             40      0
6    22            17             76      0
7    35            18              6      0
8    23            18             94      0
9    64            19              3      1
10   30            19             72      0
12   35            19             99      0
14   24            20             77      0
16   22            20             79      1
17   35            21             35      0
18   20            21             66      1
20   35            23             98      0
21   35            24             35      1
22   25            24             73      1
23   46            25              5      0
25   54            28             14      0
26   29            28             82      1
27   45            28             32      0
28   35            28             61      1
30   23            29             87      0
31   60            30              4      1
33   53            33              4      1
36   21            33             81      0
37   42            34             17      0
38   30            34             73      0
40   20            37             75      0
41   65            38             35      0
42   24            38             92      1
43   48            39             36      1
46   24            39             65      0
47   50            40             55      0
49   29            40             42      0
50   31            40             42      0
51   49            42             52      0
52   33            42             60      1
53   31            43             54      0
55   50            43             45      0
57   51            44             50      0
58   69            44             46      1
59   27            46             51      0
60   53            46             46      1
62   19            46             55      1
64   54            47             59      0
65   63            48             51      1
66   18            48             59      1
67   43            48             50      0
68   68            48             48      0
69   19            48             59      1
70   32            48             47      0
71   70            49             55      1
72   47            49             42      0
74   60            50             56      0
75   59            54             47      1
79   23            54             52      0
80   49            54             42      0
81   57            54             51      1
83   67            54             41      1
86   48            54             46      1
88   22            57             55      0
89   34            58             60      0
90   50            58             46      0
91   68            59             55      0
92   18            59             41      1
93   48            60             49      1
94   40            60             40      0
96   24            60             52      1
97   47            60             47      0
98   27            60             50      0
99   48            61             42      1
100  20            61             49      1
101  23            62             41      0
102  49            62             48      0
103  67            62             59      1
104  26            62             55      1
105  49            62             56      1
108  54            63             46      1
109  68            63             43      1
110  66            63             48      1
111  65            63             52      1
112  19            63             54      0
113  38            64             42      0
114  19            64             46      1
115  18            65             48      0
116  19            65             50      0
117  63            65             43      0
118  49            65             59      0
119  51            67             43      0
120  50            67             57      0
122  38            67             40      0
123  40            69             58      0
124  39            69             91      1
125  23            70             29      0
126  31            70             77      0
127  43            71             35      1
129  59            71             11      1
130  38            71             75      1
131  47            71              9      1
132  39            71             75      1
133  25            72             34      0
134  31            72             71      0
135  20            73              5      1
136  29            73             88      0
139  19            74             10      1
140  35            74             72      0
141  57            75              5      0
142  32            75             93      1
143  28            76             40      0
144  32            76             87      0
145  25            77             12      1
146  28            77             97      1
147  48            77             36      1
148  32            77             74      0
151  43            78             17      1
154  38            78             76      0
155  47            78             16      0
156  27            78             89      0
158  30            78             78      0
161  56            79             35      0
163  19            81              5      1
164  31            81             93      0
165  50            85             26      1
167  42            86             20      1
168  33            86             95      0
169  36            87             27      0
170  32            87             63      1
171  40            87             13      1
172  28            87             75      1
173  36            87             10      1
174  36            87             92      1
175  52            88             13      0
176  30            88             86      0
177  58            88             15      1
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$exclude
NULL

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$weights
$weights[[1]]
$weights[[1]][[1]]
           [,1]        [,2]        [,3]          [,4]
[1,] 0.64370953 11.99907497  6.39385284 -164.58821176
[2,] 1.01488201  0.06800324  0.10120237    2.36576666
[3,] 1.05156374 -0.04655805 -0.05647392   -0.09091469
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$weights[[1]][[2]]
           [,1]
[1,] -0.4072916
[2,]  1.4204539
[3,]  3.2063151
[4,] -3.1643466
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$generalized.weights
$generalized.weights[[1]]
             [,1]          [,2]          [,3]
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62  -7.554867e-36  4.215840e-36  1.179223e-34
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68  -6.095706e-29  3.401585e-29  9.514655e-28
69  -1.216322e-38  6.787436e-39  1.898530e-37
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92  -1.317871e-26  7.354113e-27  2.057037e-25
93  -8.426442e-31  4.702205e-31  1.315265e-29
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96  -6.496970e-34  3.625502e-34  1.014098e-32
97  -1.793723e-29  1.000951e-29  2.799784e-28
98  -2.073140e-32  1.156873e-32  3.235919e-31
99  -5.050541e-26  2.818352e-26  7.883279e-25
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102 -4.041799e-30  2.255443e-30  6.308755e-29
103 -7.101636e-37  3.962923e-37  1.108479e-35
104 -6.215373e-36  3.468363e-36  9.701440e-35
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108 -1.492345e-28  8.327730e-29  2.329369e-27
109 -7.035362e-26  3.925941e-26  1.098134e-24
110 -2.134156e-29  1.190922e-29  3.331157e-28
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112 -1.403818e-35  7.833722e-36  2.191189e-34
113 -1.549682e-26  8.647686e-27  2.418865e-25
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116 -6.956391e-33  3.881872e-33  1.085808e-31
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118 -9.697283e-38  5.411372e-38  1.513628e-36
119 -1.004624e-26  5.606099e-27  1.568095e-25
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122 -3.081247e-25  1.719427e-25  4.809452e-24
123 -1.510120e-37  8.426920e-38  2.357114e-36
124 -3.133452e-60  1.748559e-60  4.890937e-59
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126 -5.300582e-51  2.957882e-51  8.273563e-50
127 -1.097834e-21  6.126238e-22  1.713585e-20
129 -1.655472e-04  9.034022e-05  2.515457e-03
130 -2.396191e-49  1.337146e-49  3.740163e-48
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132 -2.651387e-49  1.479553e-49  4.138492e-48
133 -1.124194e-12  4.320195e-14 -1.974986e-12
134 -6.186706e-47  3.452365e-47  9.656694e-46
135 -5.392072e-01  2.300007e-01 -5.679732e+00
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158 -6.282464e-52  3.505801e-52  9.806161e-51
161 -2.604337e-21  1.453298e-21  4.065053e-20
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164 -3.006293e-62  1.677601e-62  4.692458e-61
165 -1.510471e-15  8.428876e-16  2.357661e-14
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168 -1.178257e-63  6.575026e-64  1.839116e-62
169 -9.464178e-11  3.637050e-12 -1.662657e-10
170 -9.031664e-42  5.039936e-42  1.409733e-40
171  7.302291e-06 -4.072352e-06 -1.138916e-04
172 -3.528452e-50  1.968981e-50  5.507483e-49
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174 -1.724499e-61  9.623218e-62  2.691734e-60
175 -6.825971e-02  2.623839e-03 -1.198965e-01
176 -1.160421e-57  6.475492e-58  1.811275e-56
177 -1.008190e-07  5.625387e-08  1.573455e-06
178 -3.938173e-46  2.197617e-46  6.147008e-45
179 -4.082824e-07  2.277756e-07  6.370819e-06
180 -2.615871e-60  1.459734e-60  4.083057e-59
181 -1.574842e-20  8.788083e-21  2.458136e-19
183 -2.963300e+00  1.138775e-01 -5.205931e+00
185 -3.324841e-25  1.855360e-25  5.189671e-24
186 -1.772068e-65  9.888665e-66  2.765982e-64
187 -2.160532e-14  1.205641e-14  3.372328e-13
190 -4.430594e-57  2.472403e-57  6.915618e-56
192 -2.800001e-46  1.562484e-46  4.370461e-45
193  2.247472e-03 -1.250261e-03 -3.430572e-02
194 -2.360009e-61  1.316955e-61  3.683686e-60
195 -5.302821e-01  2.037835e-02 -9.316007e-01
196 -2.003326e-53  1.117915e-53  3.126948e-52
197 -3.816915e-18  2.129952e-18  5.957740e-17
198 -2.837239e-50  1.583263e-50  4.428584e-49


$startweights
$startweights[[1]]
$startweights[[1]][[1]]
           [,1]         [,2]       [,3]        [,4]
[1,]  0.4437095 -0.009397413 -0.4199274 -0.71938925
[2,]  0.8148820  0.031137453 -0.7091934 -0.04007548
[3,]  0.8515637 -1.429511768  0.3394200 -0.38164388
[4,] -0.1312506 -0.627410443 -2.4553174  1.15547632

$startweights[[1]][[2]]
            [,1]
[1,] -0.34435803
[2,]  1.48338742
[3,]  0.08828987
[4,]  0.74028344
[5,] -1.06531448



$result.matrix
                                    [,1]
error                       1.615756e+01
reached.threshold           9.354496e-03
steps                       2.485400e+04
Intercept.to.1layhid1       6.437095e-01
Age.to.1layhid1             1.014882e+00
Annual_Income.to.1layhid1   1.051564e+00
Spending_Score.to.1layhid1  6.874939e-02
Intercept.to.1layhid2       1.199907e+01
Age.to.1layhid2             6.800324e-02
Annual_Income.to.1layhid2  -4.655805e-02
Spending_Score.to.1layhid2 -2.773476e+00
Intercept.to.1layhid3       6.393853e+00
Age.to.1layhid3             1.012024e-01
Annual_Income.to.1layhid3  -5.647392e-02
Spending_Score.to.1layhid3 -1.579646e+00
Intercept.to.1layhid4      -1.645882e+02
Age.to.1layhid4             2.365767e+00
Annual_Income.to.1layhid4  -9.091469e-02
Spending_Score.to.1layhid4  4.156184e+00
Intercept.to.Gender        -4.072916e-01
1layhid1.to.Gender          1.420454e+00
1layhid2.to.Gender          3.206315e+00
1layhid3.to.Gender         -3.164347e+00
1layhid4.to.Gender         -6.159011e-01

attr(,"class")
[1] "nn"
# Visualization
plot(DNNModel)


# Prediction
DNNPrediction <- predict(DNNModel, test)

# Generate Confusion Matrix
DNNconfusionMatrix <- table(DNNPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
DNNaccuracy <- round(sum(diag(DNNconfusionMatrix))/ sum(DNNconfusionMatrix), digits = 4)
cat("Deep Neural Network Model Accuracy:", DNNaccuracy)
Deep Neural Network Model Accuracy: 0
# MSE
#Metrics::mse(test, NNPrediction)

AUTO ML

localH2O = h2o.init()
 Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         3 hours 4 minutes 
    H2O cluster timezone:       America/New_York 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.32.0.1 
    H2O cluster version age:    6 months and 24 days !!! 
    H2O cluster name:           H2O_started_from_R_xiochirinos_nah310 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   1.38 GB 
    H2O cluster total cores:    8 
    H2O cluster allowed cores:  8 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4 
    R Version:                  R version 4.0.3 (2020-10-10) 

Your H2O cluster version is too old (6 months and 24 days)!
Please download and install the latest version from http://h2o.ai/download/
# Convert the data frame to an H2O Data Frame
autoMall_customers <- as.h2o(Mall_customers)

  |                                                                                                                          
  |                                                                                                                    |   0%
  |                                                                                                                          
  |====================================================================================================================| 100%
# Sample Data
autoSplit <- h2o.splitFrame(data = autoMall_customers, ratios = c(.75))
AMLtrain <- autoSplit[[1]]
AMLtestValidation <- autoSplit[[2]]

testValidationSplit <- h2o.splitFrame(data = AMLtestValidation, ratios = c(.75))
AMLtest <- testValidationSplit[[1]]
AMLvalidation <- testValidationSplit[[2]]

# AutoML
autoMLModel <- h2o.automl(y = "Gender",
                    x = c("Age", "Annual_Income", "Spending_Score"),
                    training_frame = AMLtrain,
                    validation_frame = AMLvalidation,
                    balance_classes = TRUE,
                    max_runtime_secs = 60,
                    seed = 1234)

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16:01:53.249: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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16:02:02.317: Skipping training of model GBM_5_AutoML_20210503_160153 due to exception: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for GBM model: GBM_5_AutoML_20210503_160153.  Details: ERRR on field: _min_rows: The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 148.0.

16:02:03.326: DeepLearning_1_AutoML_20210503_160153 [DeepLearning def_1] failed: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for DeepLearning model: DeepLearning_1_AutoML_20210503_160153_cv_1.  Details: ERRR on field: _balance_classes: balance_classes requires classification.

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# Prediction
AMLprediction = h2o.predict(object = autoMLModel, newdata = AMLtest)

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AutoMLtable <- as.data.frame(h2o.get_leaderboard(object = autoMLModel, extra_columns = 'ALL'))
AutoMLtable

# Performance
print(h2o.performance(autoMLModel@leader, AMLtest))
H2ORegressionMetrics: stackedensemble

MSE:  0.2491534
RMSE:  0.4991527
MAE:  0.4936293
RMSLE:  0.3473586
Mean Residual Deviance :  0.2491534
# MSE
#Metrics::mse(AMLtest, AMLprediction)

PLOTS

Mall_customers_Plot1 <- read.csv("Mall_Customers.csv")

Mall_customers_Plot1 <- Mall_customers_Plot1 %>% rename(
  Annual_Income = Annual.Income..k..,
  Spending_Score = Spending.Score..1.100.,
  Gender = Genre)

Mall_customers_Plot1 <- Mall_customers_Plot1 %>%
  select(Gender, Age, Annual_Income, Spending_Score)

Mall_customers_Plot1$Gender <- as.factor(Mall_customers_Plot1$Gender)

Mall_customers_Plot1
NA

ANIMATED PLOT

AnnualIncome_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Annual_Income, y = Spending_Score, color = Gender)) +
                                geom_line(alpha = 0.7) +
                                theme_minimal() +
                                theme(legend.position="none") +
                                theme(axis.text.x = element_text(face = "bold", size = 10)) +
                                theme(axis.text.y = element_text(face = "bold", size = 10)) +
                                theme(axis.title.x = element_text(size = 15, face = "bold"),
                                      axis.title.y = element_text(size = 15, face = "bold")) +
                                labs(title = "Annual Income vs. Spending Score",
                                    x = 'Annual Income',
                                     y = 'Spending Score') +
                                transition_reveal(Annual_Income)
                                
AnnualIncome_SpendingScore                                

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PLOTLY PLOTS

Age_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Age, y = Spending_Score, fill = Gender)) +
                          geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
                          theme_minimal() +
                          theme(legend.position="none") +
                          theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
                          theme(axis.text.y = element_text(face = "bold", size = 10)) +
                          theme(axis.title.x = element_text(size = 15, face = "bold"),
                                axis.title.y = element_text(size = 15, face = "bold")) +
                          labs(x = 'Age',
                               y = 'Spending Score')

ggplotly(Age_SpendingScore)
Age_AnnualIncome <- ggplot(Mall_customers_Plot1, aes(x = Age, y = Annual_Income, fill = Gender)) +
                          geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
                          theme_minimal() +
                          theme(legend.position="none") +
                          theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
                          theme(axis.text.y = element_text(face = "bold", size = 10)) +
                          theme(axis.title.x = element_text(size = 15, face = "bold"),
                                axis.title.y = element_text(size = 15, face = "bold")) +
                          labs(x = 'Age',
                               y = 'Annual Income')

ggplotly(Age_AnnualIncome)
rsconnect::setAccountInfo(name='xiochirinos',
              token='0F58DAD4CDCAC9786C37995CD1527BE4',
              secret='mwRCIHucKohwJ5R6Q5RLUAQkYNL0bX1LtiHn4boh')
library(rsconnect)

Attaching package: ‘rsconnect’

The following object is masked from ‘package:shiny’:

    serverInfo
rsconnect::deployApp('~/Desktop/MS_BIG_DATA/DATA VISUALIZATION/Final Project/Final_Project')
Preparing to deploy application...DONE
Uploading bundle for application: 4077877...DONE
Deploying bundle: 4556395 for application: 4077877 ...
Waiting for task: 924636169
  building: Parsing manifest
  building: Building image: 5234364
  building: Fetching packages
  building: Installing packages
  building: Installing files
  building: Pushing image: 5234364
  deploying: Starting instances
  rollforward: Activating new instances
  terminating: Stopping old instances
Application successfully deployed to https://xiochirinos.shinyapps.io/final_project/
---
title: "R Notebook"
output: html_notebook
---

```{r}
install.packages("DT")
```


```{r}
library(tidyverse)
library(forecast)
library(caTools)
library(earth)
library(randomForest)
library(kernlab)
library(h2o)
library(neuralnet)
library(Metrics)
library(caret)
library(hms)
library(lubridate)
library(ggplot2)
library(gganimate)
library(gapminder)
library(gifski)
library(png)
library(shiny)
library(plotly)
library(shinydashboard)
library(DT)
```


# DATA
```{r}
Mall_customers <- read.csv("Mall_Customers.csv", stringsAsFactors = T)

Mall_customers <- Mall_customers %>% rename(
  Annual_Income = Annual.Income..k..,
  Spending_Score = Spending.Score..1.100.)

Mall_customers <- Mall_customers %>%
  select(Genre, Age, Annual_Income, Spending_Score)

Mall_customers <- Mall_customers %>% mutate(
  Gender = if_else(Genre == "Male", true = 1, false = 0)
) %>% 
  select(-Genre)
Mall_customers
```


```{r}
summary(Mall_customers)
```


# DATA SPLIT
```{r}
set.seed(123)

sample = sample.split(Mall_customers$Gender, SplitRatio = .75)
train = subset(Mall_customers, sample == TRUE)
test = subset(Mall_customers, sample == FALSE)
```



# RAMDOM FOREST
```{r}
# Random Forest Model
RFModel <- randomForest(Gender ~ ., data = train, mtry = 3, ntree = 64)
RFModel

plot(RFModel)

# Prediction
RFPrediction <- predict(RFModel, test, type = "class")
RFPrediction

# Generate Confusion Matrix
RFconfusionMatrix <- table(RFPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
RFaccuracy <- round(sum(diag(RFconfusionMatrix))/ sum(RFconfusionMatrix), digits = 4)
cat("Random Forest Accuracy:", RFaccuracy)
```



#SVM
```{r}
# Support Vector Machine Model
SVMmodel <- ksvm(Gender ~ .,
                 data = train,
                 kernel = "vanilladot")
SVMmodel


# Prediction
SVMpred <- predict(SVMmodel, test)

# Generate Confusion Matrix
SVMconfusionMatrix <- table(SVMpred,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
SVMaccuracy <- round(sum(diag(SVMconfusionMatrix))/ sum(SVMconfusionMatrix), digits = 4)
cat("Support Vector Machine Accuracy:", SVMaccuracy)
```


# NEURAL NETWORKS
```{r}
# Neural Networks Model
NNModel <- neuralnet(Gender ~ .,
                     data = train)
NNModel

# Visualization
NNModelPlot <- plot(NNModel)
NNModelPlot

# Prediction
NNPrediction <- predict(NNModel, test)

# Generate Confusion Matrix
NNconfusionMatrix <- table(NNPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
NNaccuracy <- round(sum(diag(NNconfusionMatrix))/ sum(NNconfusionMatrix), digits = 4)
cat("Neural Network Model Accuracy:", NNaccuracy)
```


# DEEP LEARNING NEURAL NETWORKS
```{r}
# Neural Networks Model
DNNModel <- neuralnet(Gender ~ .,
                     data = train,
                     hidden = 4)
DNNModel

# Visualization
plot(DNNModel)

# Prediction
DNNPrediction <- predict(DNNModel, test)

# Generate Confusion Matrix
DNNconfusionMatrix <- table(DNNPrediction,
                         test$Gender,
                         dnn = c("Prediction", "Actual"))

# Calculate Accuracy
DNNaccuracy <- round(sum(diag(DNNconfusionMatrix))/ sum(DNNconfusionMatrix), digits = 4)
cat("Deep Neural Network Model Accuracy:", DNNaccuracy)
```




# AUTO ML
```{r}
localH2O = h2o.init()

# Convert the data frame to an H2O Data Frame
autoMall_customers <- as.h2o(Mall_customers)

# Sample Data
autoSplit <- h2o.splitFrame(data = autoMall_customers, ratios = c(.75))
AMLtrain <- autoSplit[[1]]
AMLtestValidation <- autoSplit[[2]]

testValidationSplit <- h2o.splitFrame(data = AMLtestValidation, ratios = c(.75))
AMLtest <- testValidationSplit[[1]]
AMLvalidation <- testValidationSplit[[2]]

# AutoML
autoMLModel <- h2o.automl(y = "Gender",
                    x = c("Age", "Annual_Income", "Spending_Score"),
                    training_frame = AMLtrain,
                    validation_frame = AMLvalidation,
                    balance_classes = TRUE,
                    max_runtime_secs = 60,
                    seed = 1234)

# Prediction
AMLprediction = h2o.predict(object = autoMLModel, newdata = AMLtest)

AutoMLtable <- as.data.frame(h2o.get_leaderboard(object = autoMLModel, extra_columns = 'ALL'))
AutoMLtable

# Performance
print(h2o.performance(autoMLModel@leader, AMLtest))
```


# PLOTS

```{r}
Mall_customers_Plot1 <- read.csv("Mall_Customers.csv")

Mall_customers_Plot1 <- Mall_customers_Plot1 %>% rename(
  Annual_Income = Annual.Income..k..,
  Spending_Score = Spending.Score..1.100.,
  Gender = Genre)

Mall_customers_Plot1 <- Mall_customers_Plot1 %>%
  select(Gender, Age, Annual_Income, Spending_Score)

Mall_customers_Plot1$Gender <- as.factor(Mall_customers_Plot1$Gender)

Mall_customers_Plot1

```



# ANIMATED PLOT
```{r}
AnnualIncome_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Annual_Income, y = Spending_Score, color = Gender)) +
                                geom_line(alpha = 0.7) +
                                theme_minimal() +
                                theme(legend.position="none") +
                                theme(axis.text.x = element_text(face = "bold", size = 10)) +
                                theme(axis.text.y = element_text(face = "bold", size = 10)) +
                                theme(axis.title.x = element_text(size = 15, face = "bold"),
                                      axis.title.y = element_text(size = 15, face = "bold")) +
                                labs(title = "Annual Income vs. Spending Score",
                                    x = 'Annual Income',
                                     y = 'Spending Score') +
                                transition_reveal(Annual_Income)
                                
AnnualIncome_SpendingScore                                
```


# PLOTLY PLOTS
```{r}
Age_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Age, y = Spending_Score, fill = Gender)) +
                          geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
                          theme_minimal() +
                          theme(legend.position="none") +
                          theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
                          theme(axis.text.y = element_text(face = "bold", size = 10)) +
                          theme(axis.title.x = element_text(size = 15, face = "bold"),
                                axis.title.y = element_text(size = 15, face = "bold")) +
                          labs(x = 'Age',
                               y = 'Spending Score')

ggplotly(Age_SpendingScore)
```



```{r}
Age_AnnualIncome <- ggplot(Mall_customers_Plot1, aes(x = Age, y = Annual_Income, fill = Gender)) +
                          geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
                          theme_minimal() +
                          theme(legend.position="none") +
                          theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
                          theme(axis.text.y = element_text(face = "bold", size = 10)) +
                          theme(axis.title.x = element_text(size = 15, face = "bold"),
                                axis.title.y = element_text(size = 15, face = "bold")) +
                          labs(x = 'Age',
                               y = 'Annual Income')

ggplotly(Age_AnnualIncome)
```


```{r}
rsconnect::setAccountInfo(name='xiochirinos',
			  token='0F58DAD4CDCAC9786C37995CD1527BE4',
			  secret='mwRCIHucKohwJ5R6Q5RLUAQkYNL0bX1LtiHn4boh')
```

```{r}
library(rsconnect)
rsconnect::deployApp('~/Desktop/MS_BIG_DATA/DATA VISUALIZATION/Final Project/Final_Project')
```
